Chaotic mixing characteristics in static mixers with different axial twisted‐tape inserts
Bibliographic record
Abstract
Abstract The distinction of chaotic advection and mixing of high‐viscosity fluids in tubes equipped with four different twisted tapes, including KSM, MSM, RSM, and SSM, were evaluated by Lagrangian simulation. ∼23 550 massless particle tracers marked by red and black colours were respectively released in the semicircle and concentric circles of the inlet cross‐section. Mixing performance was evaluated qualitatively by tracking Poincaré sections, and quantitatively by the variation coefficient as a function of axial position. Poincaré sections of tracers showed that KSM had the best mixing performance and RSM had two oval‐shaped segregated areas which periodically moved clockwise 90° in the cross‐sections of adjacent twisted tapes. Extensional efficiencies were computed radially and axially for all configurations. Both results showed that large dispersive mixing areas existed in the transition section. At the beginning of the first and end of the last element, the largest extensional efficiencies emerged, which were 1.07−1.21 times that at the transitions. The profiles of stretching rate showed that RSM tape had the weakest micro‐mixing ability, and the other three mixers had nearly identical stretching rates, much higher than RSM for Re < 10. With increasing Re, the mixing performance of MSM decreased first and then increased to be slightly higher than that of RSM. The secondary flows at transition regions were largely weakened for the different twist direction in the MSM. The respective roles of flow reversal and twist direction on mixing were evaluated with the stretching rates between static mixers and conventional stirred vessels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".